Evidence map›Paper›PMID 38747990›Full record

ArticleThe Journal of chemical physics2024

Seamless integration of GEM, a density based-force field, for QM/MM simulations via LICHEM, Psi4, and Tinker-HP.

Jorge Nochebuena, Andrew C Simmonett, G Andrés Cisneros

Abstract read
In one paragraph

Article in The Journal of chemical physics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Jorge NochebuenaDepartment of Physics, University of Texas at Dallas, Richardson, Texas 75080, USA.ORCID 0000-0003-0707-1066
Andrew C SimmonettLaboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA.ORCID 0000-0002-5921-9272
G Andrés CisnerosDepartment of Physics, University of Texas at Dallas, Richardson, Texas 75080, USA.ORCID 0000-0001-6629-3430

Funding

Theory and Simulation of DNA Repair Enzymes; Mechanism, Structure and FunctionR01GM108583 · NIGMS · WAYNE STATE UNIVERSITY · PI CISNEROS, GERARDO ANDRES · 2014 to 2022
$2.9M
Computational Simulations of DNA Transaction Enzymes - Application and DevelopmentR35GM151951 · NIGMS · UNIVERSITY OF TEXAS DALLAS · PI Gerardo Andres Cisneros · 2024 to 2026
$1.2M
NIGMS NIH HHS R01 GM108583NIGMS NIH HHS R35 GM151951
6 · The paper itself

Abstract

Hybrid quantum mechanics/molecular mechanics (QM/MM) simulations have become an essential tool in computational chemistry, particularly for analyzing complex biological and condensed phase systems. Building on this foundation, our work presents a novel implementation of the Gaussian Electrostatic Model (GEM), a polarizable density-based force field, within the QM/MM framework. This advancement provides seamless integration, enabling efficient and optimized QM/GEM calculations in a single step using the LICHEM Code. We have successfully applied our implementation to water dimers and hexamers, demonstrating the ability to handle water systems with varying numbers of water molecules. Moreover, we have extended the application to describe the double proton transfer of the aspartic acid dimer in a box of water, which highlights the method's proficiency in investigating heterogeneous systems. Our implementation offers the flexibility to perform on-the-fly density fitting or to utilize pre-fitted coefficients to estimate exchange and Coulomb contributions. This flexibility enhances efficiency and accuracy in modeling molecular interactions, especially in systems where polarization effects are significant.

Identifiers

PMID38747990
PMCPMC11223170

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.